Chronic viral hepatitis is clinically silent until development of cirrhosis and hepatic decompensation. Decompensated cirrhosis is a precursor to liver failure and is associated with increased risk of severe esophageal bleeding, significant cardiovascular events, and death. The gold standard for diagnosing cirrhosis is liver biopsy, though problems with the procedure include the invasive approach, sampling errors, inability to assess the severity of cirrhosis, and complications such as pain, bleeding, infection and rarely death. Furthermore, in patients with an initial diagnosis of compensated (early stage) cirrhosis, there are currently no validated noninvasive methods for predicting future hepatic decompensation. Therefore, there is a need for widely applicable noninvasive methods to diagnose cirrhosis and advanced liver fibrosis and to predict future risk of hepatic decompensation and death. We have developed a computer algorithm for measuring the amount of liver surface nodularity on routine computed tomography (CT) and magnetic resonance (MR) images. We have preliminary data providing evidence that liver surface nodularity is a useful quantitative imaging biomarker that can be used to diagnose and stage cirrhosis and to predict future hepatic decompensation and death. Major strengths of the technology include the ability to assess previously acquired liver CT and MR images (making it possible to conduct large-scale retrospective population studies), wide availability and frequent use of CT and MR imaging in cirrhosis, rapid processing time (<4 min), no requirement for intravenous contrast, no need for new hardware or special image acquisition procedures, and no measurement failures in >1200 unique patients. While our initial results are compelling, further refinement and validation are necessary prior to successful commercialization and clinical implementation of this technology for use by liver specialists. This proposal is designed to fill those gaps. Project Aim 1: Convert the Liver Surface Nodularity Software into a plugin for a FDA-approved image viewer. Converting the algorithm into a plugin for OsiriX, an open source downloadable viewer, will make the algorithm easier to use because of the ability to receive images directly from the clinical PACS, make the software easier to distribute, and will allow us to pursue the FDA 510(k) approval pathway for the plugin. Project Aim 2: Assess the accuracy of the liver surface nodularity (LSN) score for diagnosing cirrhosis and advanced liver fibrosis in patients who underwent CT-guided liver biopsy (N=100). This aim is intended to extend the applicability of the technology to avoid an invasive liver biopsy. Project Aim 3: Establish the necessary team and infrastructure to support a large-scale multi-institutional study designed to validate LSN score as a new noninvasive biomarker for chronic liver disease. Validation of the LSN score as a new noninvasive biomarker to diagnose cirrhosis and advanced liver fibrosis and predict future hepatic decompensation and death will advance the commercialization of this technology, ultimately leading to improved care for patients with chronic liver disease.